Overview

Dataset statistics

Number of variables20
Number of observations2278
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory356.1 KiB
Average record size in memory160.1 B

Variable types

Categorical3
Numeric15
Boolean2

Warnings

Churn has constant value "0" Constant
State has a high cardinality: 51 distinct values High cardinality
Total day minutes is highly correlated with Total day chargeHigh correlation
Total day charge is highly correlated with Total day minutesHigh correlation
Total eve minutes is highly correlated with Total eve chargeHigh correlation
Total eve charge is highly correlated with Total eve minutesHigh correlation
Total night minutes is highly correlated with Total night chargeHigh correlation
Total night charge is highly correlated with Total night minutesHigh correlation
Total intl minutes is highly correlated with Total intl chargeHigh correlation
Total intl charge is highly correlated with Total intl minutesHigh correlation
Area code is highly correlated with ChurnHigh correlation
Churn is highly correlated with Area code and 3 other fieldsHigh correlation
International plan is highly correlated with ChurnHigh correlation
State is highly correlated with ChurnHigh correlation
Voice mail plan is highly correlated with ChurnHigh correlation
Number vmail messages has 1610 (70.7%) zeros Zeros
Customer service calls has 476 (20.9%) zeros Zeros

Reproduction

Analysis started2021-04-10 21:21:00.680124
Analysis finished2021-04-10 21:21:25.427181
Duration24.75 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

State
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct51
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size17.9 KiB
WV
 
81
VA
 
63
AL
 
59
WY
 
58
MN
 
57
Other values (46)
1960 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4556
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK
ValueCountFrequency (%)
WV81
 
3.6%
VA63
 
2.8%
AL59
 
2.6%
WY58
 
2.5%
MN57
 
2.5%
WI57
 
2.5%
NY56
 
2.5%
OH56
 
2.5%
OR55
 
2.4%
CO52
 
2.3%
Other values (41)1684
73.9%
2021-04-10T16:21:25.602347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv81
 
3.6%
va63
 
2.8%
al59
 
2.6%
wy58
 
2.5%
wi57
 
2.5%
mn57
 
2.5%
ny56
 
2.5%
oh56
 
2.5%
or55
 
2.4%
ut52
 
2.3%
Other values (41)1684
73.9%

Most occurring characters

ValueCountFrequency (%)
N483
 
10.6%
A477
 
10.5%
M396
 
8.7%
I364
 
8.0%
T269
 
5.9%
D263
 
5.8%
O254
 
5.6%
C244
 
5.4%
V243
 
5.3%
W234
 
5.1%
Other values (14)1329
29.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4556
100.0%

Most frequent character per category

ValueCountFrequency (%)
N483
 
10.6%
A477
 
10.5%
M396
 
8.7%
I364
 
8.0%
T269
 
5.9%
D263
 
5.8%
O254
 
5.6%
C244
 
5.4%
V243
 
5.3%
W234
 
5.1%
Other values (14)1329
29.2%

Most occurring scripts

ValueCountFrequency (%)
Latin4556
100.0%

Most frequent character per script

ValueCountFrequency (%)
N483
 
10.6%
A477
 
10.5%
M396
 
8.7%
I364
 
8.0%
T269
 
5.9%
D263
 
5.8%
O254
 
5.6%
C244
 
5.4%
V243
 
5.3%
W234
 
5.1%
Other values (14)1329
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4556
100.0%

Most frequent character per block

ValueCountFrequency (%)
N483
 
10.6%
A477
 
10.5%
M396
 
8.7%
I364
 
8.0%
T269
 
5.9%
D263
 
5.8%
O254
 
5.6%
C244
 
5.4%
V243
 
5.3%
W234
 
5.1%
Other values (14)1329
29.2%

Account length
Real number (ℝ≥0)

Distinct202
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.3309921
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Memory size17.9 KiB
2021-04-10T16:21:25.694602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q173
median100
Q3127
95-th percentile165.15
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.45893632
Coefficient of variation (CV)0.3932876123
Kurtosis-0.1581933155
Mean100.3309921
Median Absolute Deviation (MAD)27
Skewness0.07601251352
Sum228554
Variance1557.007656
MonotocityNot monotonic
2021-04-10T16:21:25.812602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8731
 
1.4%
10128
 
1.2%
9328
 
1.2%
10527
 
1.2%
9027
 
1.2%
8627
 
1.2%
9927
 
1.2%
10026
 
1.1%
10625
 
1.1%
7825
 
1.1%
Other values (192)2007
88.1%
ValueCountFrequency (%)
15
0.2%
34
0.2%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
2431
< 0.1%
2251
< 0.1%
2241
< 0.1%
2211
< 0.1%
2171
< 0.1%

Area code
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.9 KiB
415
1123 
510
580 
408
575 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6834
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row415
3rd row415
4th row408
5th row415
ValueCountFrequency (%)
4151123
49.3%
510580
25.5%
408575
25.2%
2021-04-10T16:21:26.012241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-10T16:21:26.068227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4151123
49.3%
510580
25.5%
408575
25.2%

Most occurring characters

ValueCountFrequency (%)
11703
24.9%
51703
24.9%
41698
24.8%
01155
16.9%
8575
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6834
100.0%

Most frequent character per category

ValueCountFrequency (%)
11703
24.9%
51703
24.9%
41698
24.8%
01155
16.9%
8575
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common6834
100.0%

Most frequent character per script

ValueCountFrequency (%)
11703
24.9%
51703
24.9%
41698
24.8%
01155
16.9%
8575
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6834
100.0%

Most frequent character per block

ValueCountFrequency (%)
11703
24.9%
51703
24.9%
41698
24.8%
01155
16.9%
8575
 
8.4%

International plan
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
2126 
True
 
152
ValueCountFrequency (%)
False2126
93.3%
True152
 
6.7%
2021-04-10T16:21:26.108068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Voice mail plan
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
1610 
True
668 
ValueCountFrequency (%)
False1610
70.7%
True668
29.3%
2021-04-10T16:21:26.143127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Number vmail messages
Real number (ℝ≥0)

ZEROS

Distinct42
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.507462687
Minimum0
Maximum50
Zeros1610
Zeros (%)70.7%
Memory size17.9 KiB
2021-04-10T16:21:26.207843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q321
95-th percentile37
Maximum50
Range50
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.83016017
Coefficient of variation (CV)1.625650406
Kurtosis-0.2688480892
Mean8.507462687
Median Absolute Deviation (MAD)0
Skewness1.178368517
Sum19380
Variance191.2733303
MonotocityNot monotonic
2021-04-10T16:21:26.322339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
01610
70.7%
3146
 
2.0%
2838
 
1.7%
2436
 
1.6%
3033
 
1.4%
2933
 
1.4%
2533
 
1.4%
2731
 
1.4%
3331
 
1.4%
2330
 
1.3%
Other values (32)357
 
15.7%
ValueCountFrequency (%)
01610
70.7%
41
 
< 0.1%
82
 
0.1%
92
 
0.1%
101
 
< 0.1%
ValueCountFrequency (%)
502
 
0.1%
473
0.1%
463
0.1%
453
0.1%
445
0.2%

Total day minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1324
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.1043459
Minimum0
Maximum313.8
Zeros1
Zeros (%)< 0.1%
Memory size17.9 KiB
2021-04-10T16:21:26.434190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.7
Q1142.5
median177.9
Q3209.8
95-th percentile253.76
Maximum313.8
Range313.8
Interquartile range (IQR)67.3

Descriptive statistics

Standard deviation50.10533422
Coefficient of variation (CV)0.2861455777
Kurtosis0.03134419361
Mean175.1043459
Median Absolute Deviation (MAD)33.8
Skewness-0.2481924827
Sum398887.7
Variance2510.544518
MonotocityNot monotonic
2021-04-10T16:21:26.672720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183.47
 
0.3%
1856
 
0.3%
175.46
 
0.3%
159.56
 
0.3%
194.86
 
0.3%
194.45
 
0.2%
162.35
 
0.2%
206.25
 
0.2%
124.35
 
0.2%
178.75
 
0.2%
Other values (1314)2222
97.5%
ValueCountFrequency (%)
01
< 0.1%
2.61
< 0.1%
7.81
< 0.1%
7.91
< 0.1%
12.51
< 0.1%
ValueCountFrequency (%)
313.81
< 0.1%
309.91
< 0.1%
3081
< 0.1%
307.11
< 0.1%
305.21
< 0.1%

Total day calls
Real number (ℝ≥0)

Distinct114
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1593503
Minimum0
Maximum160
Zeros1
Zeros (%)< 0.1%
Memory size17.9 KiB
2021-04-10T16:21:26.788976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum160
Range160
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.6819145
Coefficient of variation (CV)0.1965060121
Kurtosis0.1403284446
Mean100.1593503
Median Absolute Deviation (MAD)13
Skewness-0.06015272982
Sum228163
Variance387.3777584
MonotocityNot monotonic
2021-04-10T16:21:26.917907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10754
 
2.4%
10554
 
2.4%
10251
 
2.2%
8850
 
2.2%
10049
 
2.2%
9849
 
2.2%
10449
 
2.2%
11248
 
2.1%
9548
 
2.1%
10847
 
2.1%
Other values (104)1779
78.1%
ValueCountFrequency (%)
01
< 0.1%
361
< 0.1%
401
< 0.1%
421
< 0.1%
441
< 0.1%
ValueCountFrequency (%)
1601
 
< 0.1%
1583
0.1%
1571
 
< 0.1%
1521
 
< 0.1%
1513
0.1%

Total day charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1324
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.76826602
Minimum0
Maximum53.35
Zeros1
Zeros (%)< 0.1%
Memory size17.9 KiB
2021-04-10T16:21:27.037132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.25
Q124.23
median30.24
Q335.67
95-th percentile43.1405
Maximum53.35
Range53.35
Interquartile range (IQR)11.44

Descriptive statistics

Standard deviation8.517839
Coefficient of variation (CV)0.2861382317
Kurtosis0.0314560443
Mean29.76826602
Median Absolute Deviation (MAD)5.75
Skewness-0.2481977403
Sum67812.11
Variance72.55358123
MonotocityNot monotonic
2021-04-10T16:21:27.157066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.187
 
0.3%
27.126
 
0.3%
31.456
 
0.3%
33.126
 
0.3%
29.826
 
0.3%
36.655
 
0.2%
28.995
 
0.2%
35.175
 
0.2%
36.725
 
0.2%
35.055
 
0.2%
Other values (1314)2222
97.5%
ValueCountFrequency (%)
01
< 0.1%
0.441
< 0.1%
1.331
< 0.1%
1.341
< 0.1%
2.131
< 0.1%
ValueCountFrequency (%)
53.351
< 0.1%
52.681
< 0.1%
52.361
< 0.1%
52.211
< 0.1%
51.881
< 0.1%

Total eve minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1325
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.8533802
Minimum0
Maximum354.2
Zeros1
Zeros (%)< 0.1%
Memory size17.9 KiB
2021-04-10T16:21:27.280287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.77
Q1163.625
median199.55
Q3233.475
95-th percentile283.3
Maximum354.2
Range354.2
Interquartile range (IQR)69.85

Descriptive statistics

Standard deviation50.81895405
Coefficient of variation (CV)0.2555599206
Kurtosis-0.01851011919
Mean198.8533802
Median Absolute Deviation (MAD)34.9
Skewness-0.02183366134
Sum452988
Variance2582.566091
MonotocityNot monotonic
2021-04-10T16:21:27.393496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167.27
 
0.3%
161.77
 
0.3%
195.56
 
0.3%
219.16
 
0.3%
205.16
 
0.3%
169.96
 
0.3%
220.66
 
0.3%
230.15
 
0.2%
241.45
 
0.2%
203.85
 
0.2%
Other values (1315)2219
97.4%
ValueCountFrequency (%)
01
< 0.1%
31.21
< 0.1%
42.21
< 0.1%
42.51
< 0.1%
43.91
< 0.1%
ValueCountFrequency (%)
354.21
< 0.1%
348.51
< 0.1%
341.31
< 0.1%
337.11
< 0.1%
335.71
< 0.1%

Total eve calls
Real number (ℝ≥0)

Distinct119
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0364355
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Memory size17.9 KiB
2021-04-10T16:21:27.515496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q187
median100
Q3114
95-th percentile134
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.25879961
Coefficient of variation (CV)0.2025142091
Kurtosis0.2571965773
Mean100.0364355
Median Absolute Deviation (MAD)13.5
Skewness-0.06574156795
Sum227883
Variance410.4189617
MonotocityNot monotonic
2021-04-10T16:21:27.635491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10555
 
2.4%
10951
 
2.2%
9450
 
2.2%
11547
 
2.1%
9747
 
2.1%
8746
 
2.0%
10846
 
2.0%
9845
 
2.0%
9945
 
2.0%
9544
 
1.9%
Other values (109)1802
79.1%
ValueCountFrequency (%)
01
< 0.1%
121
< 0.1%
361
< 0.1%
421
< 0.1%
431
< 0.1%
ValueCountFrequency (%)
1701
< 0.1%
1571
< 0.1%
1561
< 0.1%
1552
0.1%
1542
0.1%

Total eve charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1205
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.90280948
Minimum0
Maximum30.11
Zeros1
Zeros (%)< 0.1%
Memory size17.9 KiB
2021-04-10T16:21:27.769401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.0085
Q113.91
median16.965
Q319.8475
95-th percentile24.08
Maximum30.11
Range30.11
Interquartile range (IQR)5.9375

Descriptive statistics

Standard deviation4.31961408
Coefficient of variation (CV)0.2555559822
Kurtosis-0.01860933171
Mean16.90280948
Median Absolute Deviation (MAD)2.97
Skewness-0.02179073796
Sum38504.6
Variance18.6590658
MonotocityNot monotonic
2021-04-10T16:21:27.884952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.259
 
0.4%
17.438
 
0.4%
16.128
 
0.4%
17.998
 
0.4%
18.628
 
0.4%
13.747
 
0.3%
18.967
 
0.3%
18.167
 
0.3%
14.217
 
0.3%
12.956
 
0.3%
Other values (1195)2203
96.7%
ValueCountFrequency (%)
01
< 0.1%
2.651
< 0.1%
3.591
< 0.1%
3.611
< 0.1%
3.731
< 0.1%
ValueCountFrequency (%)
30.111
< 0.1%
29.621
< 0.1%
29.011
< 0.1%
28.651
< 0.1%
28.531
< 0.1%

Total night minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1333
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.4640913
Minimum43.7
Maximum395
Zeros0
Zeros (%)0.0%
Memory size17.9 KiB
2021-04-10T16:21:28.006983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum43.7
5-th percentile114.5
Q1165.825
median200
Q3235.675
95-th percentile284.505
Maximum395
Range351.3
Interquartile range (IQR)69.85

Descriptive statistics

Standard deviation51.28449606
Coefficient of variation (CV)0.2558288406
Kurtosis0.0751126226
Mean200.4640913
Median Absolute Deviation (MAD)34.9
Skewness0.03020924338
Sum456657.2
Variance2630.099536
MonotocityNot monotonic
2021-04-10T16:21:28.228468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214.66
 
0.3%
194.36
 
0.3%
214.76
 
0.3%
193.65
 
0.2%
2145
 
0.2%
190.55
 
0.2%
180.65
 
0.2%
192.75
 
0.2%
109.65
 
0.2%
172.75
 
0.2%
Other values (1323)2225
97.7%
ValueCountFrequency (%)
43.71
< 0.1%
451
< 0.1%
50.12
0.1%
53.31
< 0.1%
541
< 0.1%
ValueCountFrequency (%)
3951
< 0.1%
381.91
< 0.1%
377.51
< 0.1%
364.91
< 0.1%
364.31
< 0.1%

Total night calls
Real number (ℝ≥0)

Distinct117
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0079017
Minimum33
Maximum166
Zeros0
Zeros (%)0.0%
Memory size17.9 KiB
2021-04-10T16:21:28.349607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q187
median100
Q3113
95-th percentile131
Maximum166
Range133
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.30728173
Coefficient of variation (CV)0.1930575625
Kurtosis0.004822730703
Mean100.0079017
Median Absolute Deviation (MAD)13
Skewness0.002130693971
Sum227818
Variance372.7711277
MonotocityNot monotonic
2021-04-10T16:21:28.470303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10564
 
2.8%
10458
 
2.5%
9154
 
2.4%
9650
 
2.2%
9250
 
2.2%
10249
 
2.2%
10049
 
2.2%
10848
 
2.1%
10647
 
2.1%
9845
 
2.0%
Other values (107)1764
77.4%
ValueCountFrequency (%)
331
< 0.1%
361
< 0.1%
381
< 0.1%
421
< 0.1%
441
< 0.1%
ValueCountFrequency (%)
1661
< 0.1%
1641
< 0.1%
1572
0.1%
1562
0.1%
1551
< 0.1%

Total night charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct851
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.020974539
Minimum1.97
Maximum17.77
Zeros0
Zeros (%)0.0%
Memory size17.9 KiB
2021-04-10T16:21:28.586563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.97
5-th percentile5.15
Q17.4625
median9
Q310.6075
95-th percentile12.8045
Maximum17.77
Range15.8
Interquartile range (IQR)3.145

Descriptive statistics

Standard deviation2.307779214
Coefficient of variation (CV)0.2558237144
Kurtosis0.07475506147
Mean9.020974539
Median Absolute Deviation (MAD)1.57
Skewness0.03019732788
Sum20549.78
Variance5.3258449
MonotocityNot monotonic
2021-04-10T16:21:28.713220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6612
 
0.5%
8.579
 
0.4%
9.149
 
0.4%
9.329
 
0.4%
7.159
 
0.4%
10.499
 
0.4%
8.479
 
0.4%
9.639
 
0.4%
10.359
 
0.4%
6.488
 
0.4%
Other values (841)2186
96.0%
ValueCountFrequency (%)
1.971
< 0.1%
2.031
< 0.1%
2.252
0.1%
2.41
< 0.1%
2.431
< 0.1%
ValueCountFrequency (%)
17.771
< 0.1%
17.191
< 0.1%
16.991
< 0.1%
16.421
< 0.1%
16.391
< 0.1%

Total intl minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct156
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.13784021
Minimum0
Maximum18.9
Zeros15
Zeros (%)0.7%
Memory size17.9 KiB
2021-04-10T16:21:28.840996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.4
median10.2
Q312
95-th percentile14.6
Maximum18.9
Range18.9
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.779621729
Coefficient of variation (CV)0.274182831
Kurtosis0.7158939471
Mean10.13784021
Median Absolute Deviation (MAD)1.8
Skewness-0.2700086021
Sum23094
Variance7.726296958
MonotocityNot monotonic
2021-04-10T16:21:28.961015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1047
 
2.1%
10.243
 
1.9%
9.841
 
1.8%
10.938
 
1.7%
9.138
 
1.7%
10.638
 
1.7%
11.437
 
1.6%
9.537
 
1.6%
11.237
 
1.6%
9.936
 
1.6%
Other values (146)1886
82.8%
ValueCountFrequency (%)
015
0.7%
1.11
 
< 0.1%
1.31
 
< 0.1%
2.11
 
< 0.1%
2.21
 
< 0.1%
ValueCountFrequency (%)
18.91
< 0.1%
18.41
< 0.1%
18.22
0.1%
182
0.1%
17.82
0.1%

Total intl calls
Real number (ℝ≥0)

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.538191396
Minimum0
Maximum19
Zeros15
Zeros (%)0.7%
Memory size17.9 KiB
2021-04-10T16:21:29.066010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.85
Q13
median4
Q36
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.447532646
Coefficient of variation (CV)0.539318956
Kurtosis2.817344834
Mean4.538191396
Median Absolute Deviation (MAD)1
Skewness1.275467336
Sum10338
Variance5.990416051
MonotocityNot monotonic
2021-04-10T16:21:29.154659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3460
20.2%
4438
19.2%
5334
14.7%
2302
13.3%
6232
10.2%
7151
 
6.6%
199
 
4.3%
882
 
3.6%
973
 
3.2%
1034
 
1.5%
Other values (10)73
 
3.2%
ValueCountFrequency (%)
015
 
0.7%
199
 
4.3%
2302
13.3%
3460
20.2%
4438
19.2%
ValueCountFrequency (%)
191
< 0.1%
182
0.1%
171
< 0.1%
162
0.1%
152
0.1%

Total intl charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct156
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.737708516
Minimum0
Maximum5.1
Zeros15
Zeros (%)0.7%
Memory size17.9 KiB
2021-04-10T16:21:29.256018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.27
median2.75
Q33.24
95-th percentile3.94
Maximum5.1
Range5.1
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.7504414272
Coefficient of variation (CV)0.2741129754
Kurtosis0.7173874129
Mean2.737708516
Median Absolute Deviation (MAD)0.48
Skewness-0.2701449247
Sum6236.5
Variance0.5631623357
MonotocityNot monotonic
2021-04-10T16:21:29.374673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.747
 
2.1%
2.7543
 
1.9%
2.6541
 
1.8%
2.9438
 
1.7%
2.8638
 
1.7%
2.4638
 
1.7%
3.0237
 
1.6%
2.5737
 
1.6%
3.0837
 
1.6%
2.6736
 
1.6%
Other values (146)1886
82.8%
ValueCountFrequency (%)
015
0.7%
0.31
 
< 0.1%
0.351
 
< 0.1%
0.571
 
< 0.1%
0.591
 
< 0.1%
ValueCountFrequency (%)
5.11
< 0.1%
4.971
< 0.1%
4.912
0.1%
4.862
0.1%
4.812
0.1%

Customer service calls
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.453028973
Minimum0
Maximum7
Zeros476
Zeros (%)20.9%
Memory size17.9 KiB
2021-04-10T16:21:29.474185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.152125473
Coefficient of variation (CV)0.7929129387
Kurtosis0.9668612586
Mean1.453028973
Median Absolute Deviation (MAD)1
Skewness0.8347844656
Sum3310
Variance1.327393105
MonotocityNot monotonic
2021-04-10T16:21:29.554703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1846
37.1%
2546
24.0%
0476
20.9%
3311
 
13.7%
469
 
3.0%
520
 
0.9%
67
 
0.3%
73
 
0.1%
ValueCountFrequency (%)
0476
20.9%
1846
37.1%
2546
24.0%
3311
 
13.7%
469
 
3.0%
ValueCountFrequency (%)
73
 
0.1%
67
 
0.3%
520
 
0.9%
469
 
3.0%
3311
13.7%

Churn
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 KiB
0
2278 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2278
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
02278
100.0%
2021-04-10T16:21:29.826280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-10T16:21:29.885281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02278
100.0%

Most occurring characters

ValueCountFrequency (%)
02278
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2278
100.0%

Most frequent character per category

ValueCountFrequency (%)
02278
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2278
100.0%

Most frequent character per script

ValueCountFrequency (%)
02278
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2278
100.0%

Most frequent character per block

ValueCountFrequency (%)
02278
100.0%

Interactions

2021-04-10T16:21:01.652336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:01.749069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:01.850055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:01.952269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.050130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.149401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.249606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.463259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.571331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.677747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.779732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.880603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:02.979248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.072418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.167316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.262384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.359481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.467901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.570371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.671508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.776433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.879540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:03.980202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.076829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.176134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.280215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.381118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.476434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.585043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.687076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.784081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:04.893443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.002666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.211521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.314547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.413483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.520339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.621631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.738414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.840746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:05.945815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.042943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.143463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.246234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.352339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.476096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.591433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.711451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.827437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:06.939261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.048436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.166192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.286286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.397977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.510031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.622172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.734154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:07.840866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.041746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.137149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.236403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.337186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.434864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.532875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.631273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.735780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.837231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:08.956322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.061348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.153402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.252137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.351873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.453187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.555234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.662053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.759357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.865303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:09.967031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.082981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.185129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.290063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.398331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.517386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.723181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.829029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:10.928375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.030028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.142677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.253861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.361076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.468294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.569173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.672226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.773585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.880997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:11.988213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.094644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.196276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.310191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.407595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.516774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.625849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.732874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.827284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:12.933170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.033193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.135527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.232502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.446486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.550435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.651036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.744493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.846562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:13.950348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.053249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.159317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.266156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.367447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.486986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.591285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.694255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.797412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:14.904875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.017175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.127167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.227101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.331946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.434392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.552925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.654654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.757333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.855213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:15.964465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.160730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.270713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.377815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.485392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.591701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.701971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.811659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:16.929695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.047156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.151202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.256200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.365151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.475015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.587762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.699909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.813199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:17.926016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.033922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.145087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.257335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.361334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.476353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.582383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.702400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:18.816199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.029376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.131277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.239955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.346348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.448536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.560120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.665885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.775370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:19.898061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.019372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.157176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.269575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.375614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.482091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.593150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.700174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.809199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:20.915548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.027013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.138649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.240582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.346145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.465205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.582327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.703510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:21.913472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.019132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.147573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.253678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.350393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.452522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.551992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.647986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.746470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.842160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:22.946094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.047040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.149290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.259133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.358719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.459216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.561982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.670143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.776266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.883406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:23.989201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:24.092107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:24.196226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:24.314132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:24.433215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:24.637548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T16:21:24.744083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-10T16:21:29.964242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-10T16:21:30.213902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-10T16:21:30.442107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-10T16:21:30.692325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-10T16:21:30.915117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-10T16:21:24.954133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-10T16:21:25.295120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0KS128415NoYes25265.111045.07197.49916.78244.79111.0110.032.7010
1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.7010
2NJ137415NoNo0243.411441.38121.211010.30162.61047.3212.253.2900
3OH84408YesNo0299.47150.9061.9885.26196.9898.866.671.7820
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.7330
5AL118510YesNo0223.49837.98220.610118.75203.91189.186.361.7000
6MA121510NoYes24218.28837.09348.510829.62212.61189.577.572.0330
7MO147415YesNo0157.07926.69103.1948.76211.8969.537.161.9200
8WV141415YesYes37258.68443.96222.011118.87326.49714.6911.253.0200
9RI74415NoNo0187.712731.91163.414813.89196.0948.829.152.4600

Last rows

StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
2268SD163415YesNo0197.29033.52188.511316.02211.1949.507.882.1110
2269OK52415NoNo0124.913121.23300.511825.54192.51068.6611.643.1320
2270WY89415NoNo0115.49919.62209.911517.84280.911212.6415.964.2930
2271OH78408NoNo0193.49932.88116.9889.94243.310910.959.342.5120
2272OH96415NoNo0106.612818.12284.88724.21178.9928.0514.974.0210
2273SC79415NoNo0134.79822.90189.76816.12221.41289.9611.853.1920
2274AZ192415NoYes36156.27726.55215.512618.32279.18312.569.962.6720
2275WV68415NoNo0231.15739.29153.45513.04191.31238.619.642.5930
2276RI28510NoNo0180.810930.74288.85824.55191.9918.6414.163.8120
2277TN74415NoYes25234.411339.85265.98222.60241.47710.8613.743.7000